{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}F:\Mussolini\FPA RnR v2\Main Tables and Figures\Main Tables Log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}17 Sep 2018, 12:13:10

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table 1 "Cross-Tabulation for Autocracy and Post-Tenure Fate"
. 
. tab punish gwf_autocracy, chi column
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

    1=Post {c |}
    Tenure {c |}   1=Geddes Dataset
      Fate {c |}  codes regime as an
  Involved {c |}       autocracy
Punishment {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       612        274 {txt}{c |}{res}       886 
           {txt}{c |}{res}     88.95      54.80 {txt}{c |}{res}     74.58 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
         1 {c |}{res}        76        226 {txt}{c |}{res}       302 
           {txt}{c |}{res}     11.05      45.20 {txt}{c |}{res}     25.42 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       688        500 {txt}{c |}{res}     1,188 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}1{txt}) = {res}178.1576  {txt} Pr = {res}0.000
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table 2 "Logit Models for Negative Fates (Geddes Regimes)"
. 
. logit punish  gwf_party gwf_military gwf_monarch gwf_democracy , cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -673.4913}  
Iteration 1:{space 3}log pseudolikelihood = {res:-576.93782}  
Iteration 2:{space 3}log pseudolikelihood = {res:-572.14549}  
Iteration 3:{space 3}log pseudolikelihood = {res:-572.12351}  
Iteration 4:{space 3}log pseudolikelihood = {res:-572.12351}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,188
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}    102.61
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-572.12351{txt}{col 49}Pseudo R2{col 67}= {res}    0.1505

{txt}{ralign 79:(Std. Err. adjusted for {res:134} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}       punish{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gwf_party {c |}{col 15}{res}{space 2}-1.075248{col 27}{space 2} .2260591{col 38}{space 1}   -4.76{col 47}{space 3}0.000{col 55}{space 4}-1.518316{col 68}{space 3}  -.63218
{txt}{space 1}gwf_military {c |}{col 15}{res}{space 2}-.5922791{col 27}{space 2} .2570271{col 38}{space 1}   -2.30{col 47}{space 3}0.021{col 55}{space 4}-1.096043{col 68}{space 3}-.0885153
{txt}{space 2}gwf_monarch {c |}{col 15}{res}{space 2}-.2558069{col 27}{space 2} .6074417{col 38}{space 1}   -0.42{col 47}{space 3}0.674{col 55}{space 4}-1.446371{col 68}{space 3}  .934757
{txt}gwf_democracy {c |}{col 15}{res}{space 2}-2.486539{col 27}{space 2}  .252358{col 38}{space 1}   -9.85{col 47}{space 3}0.000{col 55}{space 4}-2.981152{col 68}{space 3}-1.991927
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .3989077{col 27}{space 2} .1548998{col 38}{space 1}    2.58{col 47}{space 3}0.010{col 55}{space 4} .0953097{col 68}{space 3} .7025057
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m1
{txt}
{com}. pre

{txt}Model reduces errors in the prediction of punish by {res}  8.94%

           {txt}{c |} Prediction of punish
    punish {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       822         64 {txt}{c |}{res}       886 
{txt}         1 {c |}{res}       211         91 {txt}{c |}{res}       302 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}     1,033        155 {txt}{c |}{res}     1,188 

{txt}Model predicts punish=0 correctly {res}93% {txt}of the time
Model predicts punish=1 correctly {res}30% {txt}of the time

{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -673.491{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -572.124
{txt}D(1183):{col 28}{res}   1144.247{col 42}{txt}LR(4):{col 69}{res}    202.736
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.151{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.143
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.157{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.231
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.224{col 42}{txt}Efron's R2:{col 69}{res}      0.174
{txt}Variance of y*:{col 28}{res}      4.237{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.769{col 42}{txt}Adj Count R2:{col 69}{res}      0.089
{txt}AIC:{col 28}{res}      0.972{col 42}{txt}AIC*n:{col 69}{res}   1154.247
{txt}BIC:{col 28}{res}  -7231.424{col 42}{txt}BIC':{col 69}{res}   -174.415
{txt}BIC used by Stata:{col 28}{res}   1179.647{col 42}{txt}AIC used by Stata:{col 69}{res}   1154.247
{txt}
{com}. 
. logit punish  gwf_party gwf_military gwf_monarch gwf_democracy max_purges irr_entry previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-480.65888}  
Iteration 1:{space 3}log pseudolikelihood = {res: -388.6465}  
Iteration 2:{space 3}log pseudolikelihood = {res:-382.95305}  
Iteration 3:{space 3}log pseudolikelihood = {res:-382.75458}  
Iteration 4:{space 3}log pseudolikelihood = {res:-382.75434}  
Iteration 5:{space 3}log pseudolikelihood = {res:-382.75434}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       921
{txt}{col 49}Wald chi2({res}8{txt}){col 67}= {res}    107.91
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-382.75434{txt}{col 49}Pseudo R2{col 67}= {res}    0.2037

{txt}{ralign 85:(Std. Err. adjusted for {res:125} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}gwf_party {c |}{col 21}{res}{space 2}-1.149043{col 33}{space 2} .3786331{col 44}{space 1}   -3.03{col 53}{space 3}0.002{col 61}{space 4}-1.891151{col 74}{space 3}-.4069361
{txt}{space 7}gwf_military {c |}{col 21}{res}{space 2}-1.753562{col 33}{space 2} .4872125{col 44}{space 1}   -3.60{col 53}{space 3}0.000{col 61}{space 4}-2.708481{col 74}{space 3}-.7986431
{txt}{space 8}gwf_monarch {c |}{col 21}{res}{space 2}-.2280328{col 33}{space 2} .6913008{col 44}{space 1}   -0.33{col 53}{space 3}0.742{col 61}{space 4}-1.582957{col 74}{space 3} 1.126892
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-2.557936{col 33}{space 2} .4047284{col 44}{space 1}   -6.32{col 53}{space 3}0.000{col 61}{space 4}-3.351189{col 74}{space 3}-1.764683
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .2070789{col 33}{space 2} .1724116{col 44}{space 1}    1.20{col 53}{space 3}0.230{col 61}{space 4}-.1308417{col 74}{space 3} .5449995
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .4131372{col 33}{space 2} .2956913{col 44}{space 1}    1.40{col 53}{space 3}0.162{col 61}{space 4}-.1664072{col 74}{space 3} .9926815
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0481984{col 33}{space 2} .0144417{col 44}{space 1}    3.34{col 53}{space 3}0.001{col 61}{space 4} .0198931{col 74}{space 3} .0765036
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2} -.529585{col 33}{space 2} .2999679{col 44}{space 1}   -1.77{col 53}{space 3}0.077{col 61}{space 4}-1.117511{col 74}{space 3} .0583413
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} .6696733{col 33}{space 2} .4623144{col 44}{space 1}    1.45{col 53}{space 3}0.147{col 61}{space 4}-.2364463{col 74}{space 3} 1.575793
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m2
{txt}
{com}. pre

{txt}Model reduces errors in the prediction of punish by {res} 20.10%

           {txt}{c |} Prediction of punish
    punish {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       685         37 {txt}{c |}{res}       722 
{txt}         1 {c |}{res}       122         77 {txt}{c |}{res}       199 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       807        114 {txt}{c |}{res}       921 

{txt}Model predicts punish=0 correctly {res}95% {txt}of the time
Model predicts punish=1 correctly {res}39% {txt}of the time

{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -480.659{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -382.754
{txt}D(912):{col 28}{res}    765.509{col 42}{txt}LR(8):{col 69}{res}    195.809
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.204{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.185
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.192{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.296
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.274{col 42}{txt}Efron's R2:{col 69}{res}      0.236
{txt}Variance of y*:{col 28}{res}      4.533{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.827{col 42}{txt}Adj Count R2:{col 69}{res}      0.201
{txt}AIC:{col 28}{res}      0.851{col 42}{txt}AIC*n:{col 69}{res}    783.509
{txt}BIC:{col 28}{res}  -5459.311{col 42}{txt}BIC':{col 69}{res}   -141.205
{txt}BIC used by Stata:{col 28}{res}    826.938{col 42}{txt}AIC used by Stata:{col 69}{res}    783.509
{txt}
{com}. 
. logit punish  gwf_party gwf_military gwf_monarch max_purges irr_entry previous_sum_punish instit_control if gwf_democracy==0, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-169.11972}  
Iteration 1:{space 3}log pseudolikelihood = {res: -156.6643}  
Iteration 2:{space 3}log pseudolikelihood = {res:-156.63255}  
Iteration 3:{space 3}log pseudolikelihood = {res:-156.63255}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       244
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     22.81
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0018
{txt}Log pseudolikelihood = {res}-156.63255{txt}{col 49}Pseudo R2{col 67}= {res}    0.0738

{txt}{ralign 85:(Std. Err. adjusted for {res:88} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}gwf_party {c |}{col 21}{res}{space 2}-1.235495{col 33}{space 2} .3409054{col 44}{space 1}   -3.62{col 53}{space 3}0.000{col 61}{space 4}-1.903658{col 74}{space 3} -.567333
{txt}{space 7}gwf_military {c |}{col 21}{res}{space 2}-1.499675{col 33}{space 2} .4287077{col 44}{space 1}   -3.50{col 53}{space 3}0.000{col 61}{space 4}-2.339927{col 74}{space 3}-.6594238
{txt}{space 8}gwf_monarch {c |}{col 21}{res}{space 2}-.5712179{col 33}{space 2} .6846812{col 44}{space 1}   -0.83{col 53}{space 3}0.404{col 61}{space 4}-1.913168{col 74}{space 3} .7707326
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0197401{col 33}{space 2} .0553931{col 44}{space 1}    0.36{col 53}{space 3}0.722{col 61}{space 4}-.0888283{col 74}{space 3} .1283085
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .3295507{col 33}{space 2} .3148613{col 44}{space 1}    1.05{col 53}{space 3}0.295{col 61}{space 4}-.2875661{col 74}{space 3} .9466674
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0138101{col 33}{space 2}  .018423{col 44}{space 1}   -0.75{col 53}{space 3}0.453{col 61}{space 4}-.0499185{col 74}{space 3} .0222982
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.4180229{col 33}{space 2} .2860214{col 44}{space 1}   -1.46{col 53}{space 3}0.144{col 61}{space 4}-.9786145{col 74}{space 3} .1425686
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 1.168324{col 33}{space 2} .4341592{col 44}{space 1}    2.69{col 53}{space 3}0.007{col 61}{space 4} .3173875{col 74}{space 3}  2.01926
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m3
{txt}
{com}. pre

{txt}Model reduces errors in the prediction of punish by {res} 28.93%

           {txt}{c |} Prediction of punish
    punish {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        95         26 {txt}{c |}{res}       121 
{txt}         1 {c |}{res}        60         63 {txt}{c |}{res}       123 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       155         89 {txt}{c |}{res}       244 

{txt}Model predicts punish=0 correctly {res}79% {txt}of the time
Model predicts punish=1 correctly {res}51% {txt}of the time

{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -169.120{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -156.633
{txt}D(236):{col 28}{res}    313.265{col 42}{txt}LR(7):{col 69}{res}     24.974
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.001
{txt}McFadden's R2:{col 28}{res}      0.074{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.027
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.097{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.130
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.125{col 42}{txt}Efron's R2:{col 69}{res}      0.098
{txt}Variance of y*:{col 28}{res}      3.759{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.648{col 42}{txt}Adj Count R2:{col 69}{res}      0.289
{txt}AIC:{col 28}{res}      1.349{col 42}{txt}AIC*n:{col 69}{res}    329.265
{txt}BIC:{col 28}{res}   -984.067{col 42}{txt}BIC':{col 69}{res}     13.506
{txt}BIC used by Stata:{col 28}{res}    357.242{col 42}{txt}AIC used by Stata:{col 69}{res}    329.265
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table 3 "Logit Models for Negative Fates (Alternative Measures)"
. 
. logit punish gwf_party  gwf_military gwf_monarch gwf_democracy pers_hybrid mil_hybrid irr_entry max_purges previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-480.65888}  
Iteration 1:{space 3}log pseudolikelihood = {res:-384.12208}  
Iteration 2:{space 3}log pseudolikelihood = {res:-377.98313}  
Iteration 3:{space 3}log pseudolikelihood = {res:-377.77103}  
Iteration 4:{space 3}log pseudolikelihood = {res:-377.77079}  
Iteration 5:{space 3}log pseudolikelihood = {res:-377.77079}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       921
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    114.96
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-377.77079{txt}{col 49}Pseudo R2{col 67}= {res}    0.2141

{txt}{ralign 85:(Std. Err. adjusted for {res:125} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}gwf_party {c |}{col 21}{res}{space 2}-1.321865{col 33}{space 2} .4014706{col 44}{space 1}   -3.29{col 53}{space 3}0.001{col 61}{space 4}-2.108733{col 74}{space 3}-.5349971
{txt}{space 7}gwf_military {c |}{col 21}{res}{space 2}-2.203396{col 33}{space 2} .5585342{col 44}{space 1}   -3.94{col 53}{space 3}0.000{col 61}{space 4}-3.298103{col 74}{space 3}-1.108689
{txt}{space 8}gwf_monarch {c |}{col 21}{res}{space 2}-.1888347{col 33}{space 2} .6906846{col 44}{space 1}   -0.27{col 53}{space 3}0.785{col 61}{space 4}-1.542552{col 74}{space 3} 1.164882
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-2.549486{col 33}{space 2} .4078117{col 44}{space 1}   -6.25{col 53}{space 3}0.000{col 61}{space 4}-3.348782{col 74}{space 3} -1.75019
{txt}{space 8}pers_hybrid {c |}{col 21}{res}{space 2} 1.255461{col 33}{space 2} .4274605{col 44}{space 1}    2.94{col 53}{space 3}0.003{col 61}{space 4} .4176543{col 74}{space 3} 2.093269
{txt}{space 9}mil_hybrid {c |}{col 21}{res}{space 2} .0074249{col 33}{space 2} .5405839{col 44}{space 1}    0.01{col 53}{space 3}0.989{col 61}{space 4}  -1.0521{col 74}{space 3}  1.06695
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .4767915{col 33}{space 2} .3177395{col 44}{space 1}    1.50{col 53}{space 3}0.133{col 61}{space 4}-.1459664{col 74}{space 3} 1.099549
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .1960784{col 33}{space 2} .1747302{col 44}{space 1}    1.12{col 53}{space 3}0.262{col 61}{space 4}-.1463865{col 74}{space 3} .5385433
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0509586{col 33}{space 2} .0146376{col 44}{space 1}    3.48{col 53}{space 3}0.000{col 61}{space 4} .0222695{col 74}{space 3} .0796477
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.5125114{col 33}{space 2} .3092585{col 44}{space 1}   -1.66{col 53}{space 3}0.097{col 61}{space 4}-1.118647{col 74}{space 3} .0936241
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} .6289327{col 33}{space 2} .4635792{col 44}{space 1}    1.36{col 53}{space 3}0.175{col 61}{space 4}-.2796659{col 74}{space 3} 1.537531
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m4
{txt}
{com}. pre

{txt}Model reduces errors in the prediction of punish by {res} 19.60%

           {txt}{c |} Prediction of punish
    punish {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       680         42 {txt}{c |}{res}       722 
{txt}         1 {c |}{res}       118         81 {txt}{c |}{res}       199 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       798        123 {txt}{c |}{res}       921 

{txt}Model predicts punish=0 correctly {res}94% {txt}of the time
Model predicts punish=1 correctly {res}41% {txt}of the time

{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -480.659{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -377.771
{txt}D(910):{col 28}{res}    755.542{col 42}{txt}LR(10):{col 69}{res}    205.776
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.214{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.191
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.200{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.309
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.282{col 42}{txt}Efron's R2:{col 69}{res}      0.249
{txt}Variance of y*:{col 28}{res}      4.581{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.826{col 42}{txt}Adj Count R2:{col 69}{res}      0.196
{txt}AIC:{col 28}{res}      0.844{col 42}{txt}AIC*n:{col 69}{res}    777.542
{txt}BIC:{col 28}{res}  -5455.627{col 42}{txt}BIC':{col 69}{res}   -137.522
{txt}BIC used by Stata:{col 28}{res}    830.622{col 42}{txt}AIC used by Stata:{col 69}{res}    777.542
{txt}
{com}. 
. logit punish max_pers_magaloni max_military_scale  gwf_democracy irr_entry  max_purges previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-364.63795}  
Iteration 1:{space 3}log pseudolikelihood = {res:-250.66209}  
Iteration 2:{space 3}log pseudolikelihood = {res:-238.50424}  
Iteration 3:{space 3}log pseudolikelihood = {res:-237.79849}  
Iteration 4:{space 3}log pseudolikelihood = {res:-237.79679}  
Iteration 5:{space 3}log pseudolikelihood = {res:-237.79679}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       721
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    141.92
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-237.79679{txt}{col 49}Pseudo R2{col 67}= {res}    0.3479

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.783029{col 33}{space 2} .2474633{col 44}{space 1}    7.21{col 53}{space 3}0.000{col 61}{space 4}  1.29801{col 74}{space 3} 2.268048
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .5644307{col 33}{space 2} .4374482{col 44}{space 1}    1.29{col 53}{space 3}0.197{col 61}{space 4}-.2929521{col 74}{space 3} 1.421813
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.0267335{col 33}{space 2}  .332763{col 44}{space 1}   -0.08{col 53}{space 3}0.936{col 61}{space 4} -.678937{col 74}{space 3}   .62547
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.6639597{col 33}{space 2} .3706343{col 44}{space 1}   -1.79{col 53}{space 3}0.073{col 61}{space 4} -1.39039{col 74}{space 3} .0624702
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0065916{col 33}{space 2} .0756964{col 44}{space 1}    0.09{col 53}{space 3}0.931{col 61}{space 4}-.1417705{col 74}{space 3} .1549538
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0184231{col 33}{space 2} .0163533{col 44}{space 1}    1.13{col 53}{space 3}0.260{col 61}{space 4}-.0136288{col 74}{space 3}  .050475
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.2968555{col 33}{space 2} .3913472{col 44}{space 1}   -0.76{col 53}{space 3}0.448{col 61}{space 4}-1.063882{col 74}{space 3}  .470171
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} -2.66789{col 33}{space 2}  .453195{col 44}{space 1}   -5.89{col 53}{space 3}0.000{col 61}{space 4}-3.556136{col 74}{space 3}-1.779644
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m5
{txt}
{com}. pre

{txt}Model reduces errors in the prediction of punish by {res} 31.97%

           {txt}{c |} Prediction of punish
    punish {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       534         40 {txt}{c |}{res}       574 
{txt}         1 {c |}{res}        60         87 {txt}{c |}{res}       147 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       594        127 {txt}{c |}{res}       721 

{txt}Model predicts punish=0 correctly {res}93% {txt}of the time
Model predicts punish=1 correctly {res}59% {txt}of the time

{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -364.638{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -237.797
{txt}D(713):{col 28}{res}    475.594{col 42}{txt}LR(7):{col 69}{res}    253.682
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.348{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.326
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.297{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.466
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.410{col 42}{txt}Efron's R2:{col 69}{res}      0.375
{txt}Variance of y*:{col 28}{res}      5.576{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.861{col 42}{txt}Adj Count R2:{col 69}{res}      0.320
{txt}AIC:{col 28}{res}      0.682{col 42}{txt}AIC*n:{col 69}{res}    491.594
{txt}BIC:{col 28}{res}  -4216.402{col 42}{txt}BIC':{col 69}{res}   -207.618
{txt}BIC used by Stata:{col 28}{res}    528.239{col 42}{txt}AIC used by Stata:{col 69}{res}    491.594
{txt}
{com}. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry  max_purges previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-353.92557}  
Iteration 1:{space 3}log pseudolikelihood = {res:-251.48986}  
Iteration 2:{space 3}log pseudolikelihood = {res:-240.25234}  
Iteration 3:{space 3}log pseudolikelihood = {res:-239.60594}  
Iteration 4:{space 3}log pseudolikelihood = {res:-239.60452}  
Iteration 5:{space 3}log pseudolikelihood = {res:-239.60452}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       730
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    141.83
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-239.60452{txt}{col 49}Pseudo R2{col 67}= {res}    0.3230

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 2.462162{col 33}{space 2} .4032583{col 44}{space 1}    6.11{col 53}{space 3}0.000{col 61}{space 4}  1.67179{col 74}{space 3} 3.252534
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 2.201438{col 33}{space 2} .3418311{col 44}{space 1}    6.44{col 53}{space 3}0.000{col 61}{space 4} 1.531461{col 74}{space 3} 2.871414
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} -.534302{col 33}{space 2} .3473276{col 44}{space 1}   -1.54{col 53}{space 3}0.124{col 61}{space 4}-1.215052{col 74}{space 3} .1464477
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-1.093398{col 33}{space 2} .4229008{col 44}{space 1}   -2.59{col 53}{space 3}0.010{col 61}{space 4}-1.922268{col 74}{space 3}-.2645276
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0384884{col 33}{space 2} .0579297{col 44}{space 1}    0.66{col 53}{space 3}0.506{col 61}{space 4}-.0750518{col 74}{space 3} .1520286
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0408799{col 33}{space 2} .0145855{col 44}{space 1}    2.80{col 53}{space 3}0.005{col 61}{space 4} .0122929{col 74}{space 3} .0694668
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.3616794{col 33}{space 2}  .359579{col 44}{space 1}   -1.01{col 53}{space 3}0.314{col 61}{space 4}-1.066441{col 74}{space 3} .3430825
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.086357{col 33}{space 2} .4326927{col 44}{space 1}   -4.82{col 53}{space 3}0.000{col 61}{space 4}-2.934419{col 74}{space 3}-1.238295
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m6
{txt}
{com}. pre

{txt}Model reduces errors in the prediction of punish by {res} 26.81%

           {txt}{c |} Prediction of punish
    punish {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       559         33 {txt}{c |}{res}       592 
{txt}         1 {c |}{res}        68         70 {txt}{c |}{res}       138 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       627        103 {txt}{c |}{res}       730 

{txt}Model predicts punish=0 correctly {res}94% {txt}of the time
Model predicts punish=1 correctly {res}51% {txt}of the time

{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -353.926{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -239.605
{txt}D(722):{col 28}{res}    479.209{col 42}{txt}LR(7):{col 69}{res}    228.642
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.323{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.300
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.269{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.433
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.382{col 42}{txt}Efron's R2:{col 69}{res}      0.351
{txt}Variance of y*:{col 28}{res}      5.323{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.862{col 42}{txt}Adj Count R2:{col 69}{res}      0.268
{txt}AIC:{col 28}{res}      0.678{col 42}{txt}AIC*n:{col 69}{res}    495.209
{txt}BIC:{col 28}{res}  -4280.969{col 42}{txt}BIC':{col 69}{res}   -182.491
{txt}BIC used by Stata:{col 28}{res}    531.953{col 42}{txt}AIC used by Stata:{col 69}{res}    495.209
{txt}
{com}. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry  max_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-214.23719}  
Iteration 1:{space 3}log pseudolikelihood = {res:-158.96326}  
Iteration 2:{space 3}log pseudolikelihood = {res:-150.14886}  
Iteration 3:{space 3}log pseudolikelihood = {res:-149.66292}  
Iteration 4:{space 3}log pseudolikelihood = {res:-149.66129}  
Iteration 5:{space 3}log pseudolikelihood = {res:-149.66129}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       453
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     91.82
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-149.66129{txt}{col 49}Pseudo R2{col 67}= {res}    0.3014

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.666807{col 33}{space 2} .5039931{col 44}{space 1}    3.31{col 53}{space 3}0.001{col 61}{space 4} .6789984{col 74}{space 3} 2.654615
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.272621{col 33}{space 2} .5260922{col 44}{space 1}    2.42{col 53}{space 3}0.016{col 61}{space 4} .2414993{col 74}{space 3} 2.303743
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.8951903{col 33}{space 2} .3830195{col 44}{space 1}   -2.34{col 53}{space 3}0.019{col 61}{space 4}-1.645895{col 74}{space 3}-.1444859
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.8776751{col 33}{space 2}  .569252{col 44}{space 1}   -1.54{col 53}{space 3}0.123{col 61}{space 4}-1.993389{col 74}{space 3} .2380384
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .4682858{col 33}{space 2} .1635549{col 44}{space 1}    2.86{col 53}{space 3}0.004{col 61}{space 4}  .147724{col 74}{space 3} .7888476
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0151312{col 33}{space 2} .0145591{col 44}{space 1}    1.04{col 53}{space 3}0.299{col 61}{space 4} -.013404{col 74}{space 3} .0436665
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.8509209{col 33}{space 2} .4657315{col 44}{space 1}   -1.83{col 53}{space 3}0.068{col 61}{space 4}-1.763738{col 74}{space 3} .0618961
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.442953{col 33}{space 2} .6845609{col 44}{space 1}   -3.57{col 53}{space 3}0.000{col 61}{space 4}-3.784668{col 74}{space 3}-1.101239
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m7
{txt}
{com}. pre

{txt}Model reduces errors in the prediction of punish by {res} 14.63%

           {txt}{c |} Prediction of punish
    punish {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       349         22 {txt}{c |}{res}       371 
{txt}         1 {c |}{res}        48         34 {txt}{c |}{res}        82 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       397         56 {txt}{c |}{res}       453 

{txt}Model predicts punish=0 correctly {res}94% {txt}of the time
Model predicts punish=1 correctly {res}41% {txt}of the time

{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -214.237{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -149.661
{txt}D(445):{col 28}{res}    299.323{col 42}{txt}LR(7):{col 69}{res}    129.152
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.301{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.264
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.248{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.406
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.395{col 42}{txt}Efron's R2:{col 69}{res}      0.303
{txt}Variance of y*:{col 28}{res}      5.439{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.845{col 42}{txt}Adj Count R2:{col 69}{res}      0.146
{txt}AIC:{col 28}{res}      0.696{col 42}{txt}AIC*n:{col 69}{res}    315.323
{txt}BIC:{col 28}{res}  -2422.249{col 42}{txt}BIC':{col 69}{res}    -86.341
{txt}BIC used by Stata:{col 28}{res}    348.250{col 42}{txt}AIC used by Stata:{col 69}{res}    315.323
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Substantive effects (within text, associated with Table 3)
. logit punish c.max_pers_magaloni c.max_military_scale gwf_democracy irr_entry  c.max_both_max_pts previous_sum_punish i.instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-224.53003}  
Iteration 1:{space 3}log pseudolikelihood = {res:-161.69118}  
Iteration 2:{space 3}log pseudolikelihood = {res:-151.95041}  
Iteration 3:{space 3}log pseudolikelihood = {res: -151.4439}  
Iteration 4:{space 3}log pseudolikelihood = {res:-151.44202}  
Iteration 5:{space 3}log pseudolikelihood = {res:-151.44202}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       460
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     83.67
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-151.44202{txt}{col 49}Pseudo R2{col 67}= {res}    0.3255

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.300437{col 33}{space 2}  .289592{col 44}{space 1}    4.49{col 53}{space 3}0.000{col 61}{space 4} .7328475{col 74}{space 3} 1.868027
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .2495593{col 33}{space 2} .5549347{col 44}{space 1}    0.45{col 53}{space 3}0.653{col 61}{space 4}-.8380927{col 74}{space 3} 1.337211
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.3298539{col 33}{space 2} .3988475{col 44}{space 1}   -0.83{col 53}{space 3}0.408{col 61}{space 4}-1.111581{col 74}{space 3} .4518729
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.6104287{col 33}{space 2} .4822467{col 44}{space 1}   -1.27{col 53}{space 3}0.206{col 61}{space 4}-1.555615{col 74}{space 3} .3347574
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .4431131{col 33}{space 2} .1500587{col 44}{space 1}    2.95{col 53}{space 3}0.003{col 61}{space 4} .1490034{col 74}{space 3} .7372228
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0032282{col 33}{space 2} .0151777{col 44}{space 1}    0.21{col 53}{space 3}0.832{col 61}{space 4}-.0265196{col 74}{space 3}  .032976
{txt}{space 3}1.instit_control {c |}{col 21}{res}{space 2}-.9054129{col 33}{space 2} .4949681{col 44}{space 1}   -1.83{col 53}{space 3}0.067{col 61}{space 4}-1.875533{col 74}{space 3} .0647068
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.924196{col 33}{space 2} .6698176{col 44}{space 1}   -4.37{col 53}{space 3}0.000{col 61}{space 4}-4.237014{col 74}{space 3}-1.611377
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, at(max_pers_magaloni=(1(1)2) max_military_scale=0.26 gwf_democracy=0 irr_entry=0 max_both_max_pts=3 instit_control=1 previous_sum_punish=3)
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}       460
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(punish), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:max_pers_m~i}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:max_milita~e}{space 4}{txt:=} {space 8}.26}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:gwf_democr~y}{space 4}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:irr_entry}{space 7}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:max_both_m~s}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:previous_s~h}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:instit_control}{space 2}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:max_pers_m~i}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:max_milita~e}{space 4}{txt:=} {space 8}.26}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:gwf_democr~y}{space 4}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:irr_entry}{space 7}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:max_both_m~s}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:previous_s~h}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:instit_control}{space 2}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2450366{col 26}{space 2} .0560688{col 37}{space 1}    4.37{col 46}{space 3}0.000{col 54}{space 4} .1351437{col 67}{space 3} .3549295
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5436822{col 26}{space 2} .0935457{col 37}{space 1}    5.81{col 46}{space 3}0.000{col 54}{space 4}  .360336{col 67}{space 3} .7270285
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. logit punish c.max_person_scale c.max_military_scale gwf_democracy irr_entry  c.max_both_max_pts previous_sum_punish i.instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-214.23719}  
Iteration 1:{space 3}log pseudolikelihood = {res:-158.96326}  
Iteration 2:{space 3}log pseudolikelihood = {res:-150.14886}  
Iteration 3:{space 3}log pseudolikelihood = {res:-149.66292}  
Iteration 4:{space 3}log pseudolikelihood = {res:-149.66129}  
Iteration 5:{space 3}log pseudolikelihood = {res:-149.66129}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       453
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     91.82
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-149.66129{txt}{col 49}Pseudo R2{col 67}= {res}    0.3014

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.666807{col 33}{space 2} .5039931{col 44}{space 1}    3.31{col 53}{space 3}0.001{col 61}{space 4} .6789984{col 74}{space 3} 2.654615
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.272621{col 33}{space 2} .5260922{col 44}{space 1}    2.42{col 53}{space 3}0.016{col 61}{space 4} .2414993{col 74}{space 3} 2.303743
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.8951903{col 33}{space 2} .3830195{col 44}{space 1}   -2.34{col 53}{space 3}0.019{col 61}{space 4}-1.645895{col 74}{space 3}-.1444859
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.8776751{col 33}{space 2}  .569252{col 44}{space 1}   -1.54{col 53}{space 3}0.123{col 61}{space 4}-1.993389{col 74}{space 3} .2380384
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .4682858{col 33}{space 2} .1635549{col 44}{space 1}    2.86{col 53}{space 3}0.004{col 61}{space 4}  .147724{col 74}{space 3} .7888476
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0151312{col 33}{space 2} .0145591{col 44}{space 1}    1.04{col 53}{space 3}0.299{col 61}{space 4} -.013404{col 74}{space 3} .0436665
{txt}{space 3}1.instit_control {c |}{col 21}{res}{space 2}-.8509209{col 33}{space 2} .4657315{col 44}{space 1}   -1.83{col 53}{space 3}0.068{col 61}{space 4}-1.763738{col 74}{space 3} .0618961
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.442953{col 33}{space 2} .6845609{col 44}{space 1}   -3.57{col 53}{space 3}0.000{col 61}{space 4}-3.784668{col 74}{space 3}-1.101239
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, at(max_person_scale=(0.5(0.5)1) max_military_scale=0.26 gwf_democracy=0 irr_entry=0 max_both_max_pts=3 instit_control=1 previous_sum_punish=3)
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}       453
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(punish), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:max_person~e}{space 4}{txt:=} {space 9}.5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:max_milita~e}{space 4}{txt:=} {space 8}.26}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:gwf_democr~y}{space 4}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:irr_entry}{space 7}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:max_both_m~s}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:previous_s~h}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:instit_control}{space 2}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:max_person~e}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:max_milita~e}{space 4}{txt:=} {space 8}.26}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:gwf_democr~y}{space 4}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:irr_entry}{space 7}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:max_both_m~s}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:previous_s~h}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:instit_control}{space 2}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  .336409{col 26}{space 2} .0880392{col 37}{space 1}    3.82{col 46}{space 3}0.000{col 54}{space 4} .1638554{col 67}{space 3} .5089626
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5384403{col 26}{space 2} .1406986{col 37}{space 1}    3.83{col 46}{space 3}0.000{col 54}{space 4} .2626761{col 67}{space 3} .8142045
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. 
. *Table 4 "Multinomial Probit Models for Negative Fates"
. 
. mprobit posttenurefate max_person_scale max_military_scale  gwf_democracy irr_entry max_purges previous_sum_punish  instit_control, base(0) cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-374.48356}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-372.57487}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-372.47905}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-372.47881}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-372.47881}  
{res}
{txt}Multinomial probit regression{col 49}Number of obs{col 67}= {res}       730
{txt}{col 49}Wald chi2({res}21{txt}){col 67}= {res}    208.67
{txt}Log pseudolikelihood = {res}-372.47881{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}     posttenurefate{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0                  {col 21}{txt}{c |}  (base outcome)
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                   {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.725852{col 33}{space 2} .3761108{col 44}{space 1}    4.59{col 53}{space 3}0.000{col 61}{space 4} .9886886{col 74}{space 3} 2.463016
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.561942{col 33}{space 2} .2782837{col 44}{space 1}    5.61{col 53}{space 3}0.000{col 61}{space 4} 1.016516{col 74}{space 3} 2.107368
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.6020674{col 33}{space 2} .3092437{col 44}{space 1}   -1.95{col 53}{space 3}0.052{col 61}{space 4}-1.208174{col 74}{space 3} .0040391
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.7101885{col 33}{space 2} .3518384{col 44}{space 1}   -2.02{col 53}{space 3}0.044{col 61}{space 4}-1.399779{col 74}{space 3}-.0205979
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0327692{col 33}{space 2} .0490703{col 44}{space 1}    0.67{col 53}{space 3}0.504{col 61}{space 4}-.0634069{col 74}{space 3} .1289452
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0525953{col 33}{space 2} .0140752{col 44}{space 1}    3.74{col 53}{space 3}0.000{col 61}{space 4} .0250085{col 74}{space 3} .0801822
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.4859509{col 33}{space 2}  .322298{col 44}{space 1}   -1.51{col 53}{space 3}0.132{col 61}{space 4}-1.117643{col 74}{space 3} .1457416
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.070827{col 33}{space 2} .4194731{col 44}{space 1}   -4.94{col 53}{space 3}0.000{col 61}{space 4}-2.892979{col 74}{space 3}-1.248675
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2                   {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.740628{col 33}{space 2} .3327382{col 44}{space 1}    5.23{col 53}{space 3}0.000{col 61}{space 4} 1.088473{col 74}{space 3} 2.392783
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.713453{col 33}{space 2}  .326836{col 44}{space 1}    5.24{col 53}{space 3}0.000{col 61}{space 4} 1.072866{col 74}{space 3}  2.35404
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.3011141{col 33}{space 2} .3363416{col 44}{space 1}   -0.90{col 53}{space 3}0.371{col 61}{space 4}-.9603315{col 74}{space 3} .3581033
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.9010447{col 33}{space 2} .3137137{col 44}{space 1}   -2.87{col 53}{space 3}0.004{col 61}{space 4}-1.515912{col 74}{space 3}-.2861772
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0227351{col 33}{space 2} .0503172{col 44}{space 1}    0.45{col 53}{space 3}0.651{col 61}{space 4}-.0758848{col 74}{space 3}  .121355
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0170119{col 33}{space 2} .0164205{col 44}{space 1}    1.04{col 53}{space 3}0.300{col 61}{space 4}-.0151718{col 74}{space 3} .0491955
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2} .0729239{col 33}{space 2} .3429932{col 44}{space 1}    0.21{col 53}{space 3}0.832{col 61}{space 4}-.5993304{col 74}{space 3} .7451783
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.528851{col 33}{space 2} .4226045{col 44}{space 1}   -5.98{col 53}{space 3}0.000{col 61}{space 4}-3.357141{col 74}{space 3}-1.700561
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}3                   {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 2.123835{col 33}{space 2} .4378125{col 44}{space 1}    4.85{col 53}{space 3}0.000{col 61}{space 4} 1.265739{col 74}{space 3} 2.981932
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.307535{col 33}{space 2} .4364965{col 44}{space 1}    3.00{col 53}{space 3}0.003{col 61}{space 4} .4520172{col 74}{space 3} 2.163052
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.1944172{col 33}{space 2} .3682403{col 44}{space 1}   -0.53{col 53}{space 3}0.598{col 61}{space 4}-.9161549{col 74}{space 3} .5273205
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.7940961{col 33}{space 2} .4002443{col 44}{space 1}   -1.98{col 53}{space 3}0.047{col 61}{space 4}-1.578561{col 74}{space 3}-.0096316
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0467871{col 33}{space 2} .0455796{col 44}{space 1}    1.03{col 53}{space 3}0.305{col 61}{space 4}-.0425473{col 74}{space 3} .1361214
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0194291{col 33}{space 2} .0280555{col 44}{space 1}   -0.69{col 53}{space 3}0.489{col 61}{space 4}-.0744169{col 74}{space 3} .0355588
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.3219082{col 33}{space 2} .4051509{col 44}{space 1}   -0.79{col 53}{space 3}0.427{col 61}{space 4}-1.115989{col 74}{space 3} .4721729
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.556593{col 33}{space 2} .4086926{col 44}{space 1}   -6.26{col 53}{space 3}0.000{col 61}{space 4}-3.357616{col 74}{space 3}-1.755571
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m8
{txt}
{com}. 
. mprobit posttenurefate max_person_scale max_military_scale  gwf_democracy irr_entry max_both_max_pts previous_sum_punish  instit_control, base(0) cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-229.35968}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-227.79027}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-227.66915}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-227.66887}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-227.66887}  
{res}
{txt}Multinomial probit regression{col 49}Number of obs{col 67}= {res}       453
{txt}{col 49}Wald chi2({res}21{txt}){col 67}= {res}    139.62
{txt}Log pseudolikelihood = {res}-227.66887{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}     posttenurefate{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0                  {col 21}{txt}{c |}  (base outcome)
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                   {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.226144{col 33}{space 2} .5172929{col 44}{space 1}    2.37{col 53}{space 3}0.018{col 61}{space 4} .2122681{col 74}{space 3} 2.240019
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .6238515{col 33}{space 2} .4164074{col 44}{space 1}    1.50{col 53}{space 3}0.134{col 61}{space 4}-.1922921{col 74}{space 3} 1.439995
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.7484729{col 33}{space 2} .4020611{col 44}{space 1}   -1.86{col 53}{space 3}0.063{col 61}{space 4}-1.536498{col 74}{space 3} .0395524
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.3939903{col 33}{space 2}  .483358{col 44}{space 1}   -0.82{col 53}{space 3}0.415{col 61}{space 4}-1.341355{col 74}{space 3} .5533739
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .4477985{col 33}{space 2}  .159359{col 44}{space 1}    2.81{col 53}{space 3}0.005{col 61}{space 4} .1354605{col 74}{space 3} .7601364
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0329269{col 33}{space 2} .0166908{col 44}{space 1}    1.97{col 53}{space 3}0.049{col 61}{space 4} .0002135{col 74}{space 3} .0656403
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-1.029328{col 33}{space 2} .4351457{col 44}{space 1}   -2.37{col 53}{space 3}0.018{col 61}{space 4}-1.882198{col 74}{space 3}-.1764584
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.703053{col 33}{space 2} .6836652{col 44}{space 1}   -3.95{col 53}{space 3}0.000{col 61}{space 4}-4.043012{col 74}{space 3}-1.363094
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2                   {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.102879{col 33}{space 2} .4087229{col 44}{space 1}    2.70{col 53}{space 3}0.007{col 61}{space 4}  .301797{col 74}{space 3} 1.903961
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.236211{col 33}{space 2} .5026057{col 44}{space 1}    2.46{col 53}{space 3}0.014{col 61}{space 4} .2511223{col 74}{space 3} 2.221301
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.4353254{col 33}{space 2} .4063412{col 44}{space 1}   -1.07{col 53}{space 3}0.284{col 61}{space 4} -1.23174{col 74}{space 3} .3610888
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.7428022{col 33}{space 2} .4314999{col 44}{space 1}   -1.72{col 53}{space 3}0.085{col 61}{space 4}-1.588526{col 74}{space 3}  .102922
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .3299745{col 33}{space 2} .1351711{col 44}{space 1}    2.44{col 53}{space 3}0.015{col 61}{space 4}  .065044{col 74}{space 3}  .594905
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0049405{col 33}{space 2} .0162188{col 44}{space 1}    0.30{col 53}{space 3}0.761{col 61}{space 4}-.0268478{col 74}{space 3} .0367288
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.3063626{col 33}{space 2} .4267853{col 44}{space 1}   -0.72{col 53}{space 3}0.473{col 61}{space 4}-1.142846{col 74}{space 3} .5301212
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.830968{col 33}{space 2}  .654279{col 44}{space 1}   -4.33{col 53}{space 3}0.000{col 61}{space 4}-4.113331{col 74}{space 3}-1.548605
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}3                   {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.617046{col 33}{space 2} .4814708{col 44}{space 1}    3.36{col 53}{space 3}0.001{col 61}{space 4} .6733809{col 74}{space 3} 2.560712
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .7209448{col 33}{space 2} .5238764{col 44}{space 1}    1.38{col 53}{space 3}0.169{col 61}{space 4} -.305834{col 74}{space 3} 1.747724
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.8432149{col 33}{space 2} .3545148{col 44}{space 1}   -2.38{col 53}{space 3}0.017{col 61}{space 4}-1.538051{col 74}{space 3}-.1483787
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.7189574{col 33}{space 2} .5327223{col 44}{space 1}   -1.35{col 53}{space 3}0.177{col 61}{space 4}-1.763074{col 74}{space 3} .3251591
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .1773412{col 33}{space 2} .1649448{col 44}{space 1}    1.08{col 53}{space 3}0.282{col 61}{space 4}-.1459447{col 74}{space 3} .5006271
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0269186{col 33}{space 2} .0329212{col 44}{space 1}   -0.82{col 53}{space 3}0.414{col 61}{space 4} -.091443{col 74}{space 3} .0376058
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.3826828{col 33}{space 2} .5263714{col 44}{space 1}   -0.73{col 53}{space 3}0.467{col 61}{space 4}-1.414352{col 74}{space 3} .6489862
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.298157{col 33}{space 2} .6433314{col 44}{space 1}   -3.57{col 53}{space 3}0.000{col 61}{space 4}-3.559063{col 74}{space 3}-1.037251
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m9
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table 5 "Bivariate Probit Models for Negative Fates and Irregular Exit"
. 
. biprobit punish irr_exit  max_person_scale max_military_scale gwf_democracy irr_entry  max_purges previous_sum_punish instit_control, cluster(ccode)

{txt}Fitting comparison equation 1:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-353.92557}  
Iteration 1:{space 3}log pseudolikelihood = {res:-239.65483}  
Iteration 2:{space 3}log pseudolikelihood = {res:-238.46184}  
Iteration 3:{space 3}log pseudolikelihood = {res:-238.46004}  
Iteration 4:{space 3}log pseudolikelihood = {res:-238.46004}  
{res}
{txt}Fitting comparison equation 2:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-332.63832}  
Iteration 1:{space 3}log pseudolikelihood = {res:-206.48341}  
Iteration 2:{space 3}log pseudolikelihood = {res:-204.24194}  
Iteration 3:{space 3}log pseudolikelihood = {res:-204.23166}  
Iteration 4:{space 3}log pseudolikelihood = {res:-204.23166}  
{res}
{txt}Comparison:    log pseudolikelihood = {res}-442.69169

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-442.69169}  
Iteration 1:{space 3}log pseudolikelihood = {res:-372.55251}  
Iteration 2:{space 3}log pseudolikelihood = {res:-368.39837}  
Iteration 3:{space 3}log pseudolikelihood = {res:-368.34121}  
Iteration 4:{space 3}log pseudolikelihood = {res:-368.34119}  
{res}
{txt}Bivariate probit regression{col 49}Number of obs{col 67}= {res}       730
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    271.05
{txt}Log pseudolikelihood = {res}-368.34119{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.407961{col 33}{space 2}  .229979{col 44}{space 1}    6.12{col 53}{space 3}0.000{col 61}{space 4} .9572108{col 74}{space 3} 1.858712
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.192723{col 33}{space 2}   .19484{col 44}{space 1}    6.12{col 53}{space 3}0.000{col 61}{space 4} .8108433{col 74}{space 3} 1.574602
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.3568761{col 33}{space 2} .1825511{col 44}{space 1}   -1.95{col 53}{space 3}0.051{col 61}{space 4}-.7146697{col 74}{space 3} .0009175
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.6159648{col 33}{space 2} .2357525{col 44}{space 1}   -2.61{col 53}{space 3}0.009{col 61}{space 4}-1.078031{col 74}{space 3}-.1538984
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0285464{col 33}{space 2} .0363343{col 44}{space 1}    0.79{col 53}{space 3}0.432{col 61}{space 4}-.0426675{col 74}{space 3} .0997602
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0244312{col 33}{space 2} .0074085{col 44}{space 1}    3.30{col 53}{space 3}0.001{col 61}{space 4} .0099107{col 74}{space 3} .0389517
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.1960969{col 33}{space 2} .1992477{col 44}{space 1}   -0.98{col 53}{space 3}0.325{col 61}{space 4}-.5866151{col 74}{space 3} .1944213
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.170315{col 33}{space 2} .2327317{col 44}{space 1}   -5.03{col 53}{space 3}0.000{col 61}{space 4}-1.626461{col 74}{space 3}-.7141693
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}irr_exit            {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.186459{col 33}{space 2} .2185621{col 44}{space 1}    5.43{col 53}{space 3}0.000{col 61}{space 4} .7580848{col 74}{space 3} 1.614833
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2}  1.76892{col 33}{space 2} .1870142{col 44}{space 1}    9.46{col 53}{space 3}0.000{col 61}{space 4} 1.402379{col 74}{space 3} 2.135461
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.2752181{col 33}{space 2} .2058513{col 44}{space 1}   -1.34{col 53}{space 3}0.181{col 61}{space 4}-.6786791{col 74}{space 3}  .128243
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.6319881{col 33}{space 2} .2109428{col 44}{space 1}   -3.00{col 53}{space 3}0.003{col 61}{space 4}-1.045428{col 74}{space 3}-.2185479
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0159489{col 33}{space 2} .0350221{col 44}{space 1}    0.46{col 53}{space 3}0.649{col 61}{space 4}-.0526931{col 74}{space 3} .0845909
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0006316{col 33}{space 2} .0119339{col 44}{space 1}    0.05{col 53}{space 3}0.958{col 61}{space 4}-.0227585{col 74}{space 3} .0240217
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.0721402{col 33}{space 2} .2111001{col 44}{space 1}   -0.34{col 53}{space 3}0.733{col 61}{space 4}-.4858888{col 74}{space 3} .3416083
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.454658{col 33}{space 2} .2764909{col 44}{space 1}   -5.26{col 53}{space 3}0.000{col 61}{space 4}-1.996571{col 74}{space 3} -.912746
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} 1.390217{col 33}{space 2}   .14844{col 44}{space 1}    9.37{col 53}{space 3}0.000{col 61}{space 4}  1.09928{col 74}{space 3} 1.681154
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .8832186{col 33}{space 2} .0326456{col 61}{space 4} .8002402{col 74}{space 3} .9330111
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of rho=0: chi2({res}1{txt}) = {res}87.7129                     {txt}Prob > chi2 = {res}0.0000
{txt}
{com}. est store m10 
{txt}
{com}. 
. biprobit punish irr_exit  max_pers_magaloni max_military_scale gwf_democracy irr_entry  max_purges previous_sum_punish instit_control, cluster(ccode)

{txt}Fitting comparison equation 1:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-364.63795}  
Iteration 1:{space 3}log pseudolikelihood = {res:-238.18197}  
Iteration 2:{space 3}log pseudolikelihood = {res:-236.45585}  
Iteration 3:{space 3}log pseudolikelihood = {res:-236.45113}  
Iteration 4:{space 3}log pseudolikelihood = {res:-236.45113}  
{res}
{txt}Fitting comparison equation 2:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-341.71767}  
Iteration 1:{space 3}log pseudolikelihood = {res:-205.87704}  
Iteration 2:{space 3}log pseudolikelihood = {res:-203.00589}  
Iteration 3:{space 3}log pseudolikelihood = {res:-202.99728}  
Iteration 4:{space 3}log pseudolikelihood = {res:-202.99728}  
{res}
{txt}Comparison:    log pseudolikelihood = {res}-439.44841

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-439.44841}  
Iteration 1:{space 3}log pseudolikelihood = {res:-365.64033}  
Iteration 2:{space 3}log pseudolikelihood = {res: -361.0504}  
Iteration 3:{space 3}log pseudolikelihood = {res: -360.9704}  
Iteration 4:{space 3}log pseudolikelihood = {res:-360.97037}  
{res}
{txt}Bivariate probit regression{col 49}Number of obs{col 67}= {res}       721
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    297.77
{txt}Log pseudolikelihood = {res}-360.97037{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.029884{col 33}{space 2} .1366553{col 44}{space 1}    7.54{col 53}{space 3}0.000{col 61}{space 4} .7620449{col 74}{space 3} 1.297724
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .3232398{col 33}{space 2} .2517665{col 44}{space 1}    1.28{col 53}{space 3}0.199{col 61}{space 4}-.1702135{col 74}{space 3} .8166931
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .0295932{col 33}{space 2} .1836003{col 44}{space 1}    0.16{col 53}{space 3}0.872{col 61}{space 4}-.3302567{col 74}{space 3} .3894431
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.3498319{col 33}{space 2} .2073554{col 44}{space 1}   -1.69{col 53}{space 3}0.092{col 61}{space 4} -.756241{col 74}{space 3} .0565772
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0089769{col 33}{space 2} .0411416{col 44}{space 1}    0.22{col 53}{space 3}0.827{col 61}{space 4}-.0716591{col 74}{space 3}  .089613
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}   .01203{col 33}{space 2} .0083464{col 44}{space 1}    1.44{col 53}{space 3}0.149{col 61}{space 4}-.0043286{col 74}{space 3} .0283886
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.2070634{col 33}{space 2} .2254892{col 44}{space 1}   -0.92{col 53}{space 3}0.358{col 61}{space 4}-.6490141{col 74}{space 3} .2348872
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.543807{col 33}{space 2} .2553659{col 44}{space 1}   -6.05{col 53}{space 3}0.000{col 61}{space 4}-2.044315{col 74}{space 3}-1.043299
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}irr_exit            {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} .9373357{col 33}{space 2} .1323266{col 44}{space 1}    7.08{col 53}{space 3}0.000{col 61}{space 4} .6779804{col 74}{space 3} 1.196691
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .9998119{col 33}{space 2} .2766225{col 44}{space 1}    3.61{col 53}{space 3}0.000{col 61}{space 4} .4576419{col 74}{space 3} 1.541982
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .1588119{col 33}{space 2} .2176188{col 44}{space 1}    0.73{col 53}{space 3}0.466{col 61}{space 4}-.2677132{col 74}{space 3}  .585337
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.4129079{col 33}{space 2} .1969409{col 44}{space 1}   -2.10{col 53}{space 3}0.036{col 61}{space 4}-.7989049{col 74}{space 3}-.0269108
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0015734{col 33}{space 2}  .039228{col 44}{space 1}    0.04{col 53}{space 3}0.968{col 61}{space 4}-.0753121{col 74}{space 3}  .078459
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} -.010131{col 33}{space 2} .0138995{col 44}{space 1}   -0.73{col 53}{space 3}0.466{col 61}{space 4}-.0373735{col 74}{space 3} .0171115
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2} -.131091{col 33}{space 2} .2168596{col 44}{space 1}   -0.60{col 53}{space 3}0.546{col 61}{space 4}-.5561281{col 74}{space 3} .2939461
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}  -1.8424{col 33}{space 2} .2723233{col 44}{space 1}   -6.77{col 53}{space 3}0.000{col 61}{space 4}-2.376144{col 74}{space 3}-1.308656
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} 1.431979{col 33}{space 2} .1586747{col 44}{space 1}    9.02{col 53}{space 3}0.000{col 61}{space 4} 1.120982{col 74}{space 3} 1.742975
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .8920713{col 33}{space 2} .0324028{col 61}{space 4} .8079102{col 74}{space 3} .9405707
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of rho=0: chi2({res}1{txt}) = {res}81.4437                     {txt}Prob > chi2 = {res}0.0000
{txt}
{com}. est store m11
{txt}
{com}. 
. biprobit punish irr_exit  max_person_scale max_military_scale gwf_democracy  irr_entry  max_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{txt}Fitting comparison equation 1:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-214.23719}  
Iteration 1:{space 3}log pseudolikelihood = {res:-150.23018}  
Iteration 2:{space 3}log pseudolikelihood = {res:-148.54319}  
Iteration 3:{space 3}log pseudolikelihood = {res:-148.53551}  
Iteration 4:{space 3}log pseudolikelihood = {res:-148.53551}  
{res}
{txt}Fitting comparison equation 2:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-193.29904}  
Iteration 1:{space 3}log pseudolikelihood = {res:-128.61069}  
Iteration 2:{space 3}log pseudolikelihood = {res:   -127.01}  
Iteration 3:{space 3}log pseudolikelihood = {res: -127.0017}  
Iteration 4:{space 3}log pseudolikelihood = {res: -127.0017}  
{res}
{txt}Comparison:    log pseudolikelihood = {res} -275.5372

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -275.5372}  
Iteration 1:{space 3}log pseudolikelihood = {res:-232.98512}  
Iteration 2:{space 3}log pseudolikelihood = {res:-230.53015}  
Iteration 3:{space 3}log pseudolikelihood = {res:-230.49022}  
Iteration 4:{space 3}log pseudolikelihood = {res:-230.49021}  
{res}
{txt}Bivariate probit regression{col 49}Number of obs{col 67}= {res}       453
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    211.37
{txt}Log pseudolikelihood = {res}-230.49021{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} .9536819{col 33}{space 2} .2992939{col 44}{space 1}    3.19{col 53}{space 3}0.001{col 61}{space 4} .3670767{col 74}{space 3} 1.540287
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .7075904{col 33}{space 2} .2844936{col 44}{space 1}    2.49{col 53}{space 3}0.013{col 61}{space 4} .1499931{col 74}{space 3} 1.265188
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.5027421{col 33}{space 2} .2049716{col 44}{space 1}   -2.45{col 53}{space 3}0.014{col 61}{space 4} -.904479{col 74}{space 3}-.1010052
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.4621521{col 33}{space 2}  .312478{col 44}{space 1}   -1.48{col 53}{space 3}0.139{col 61}{space 4}-1.074598{col 74}{space 3} .1502935
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .2799251{col 33}{space 2}   .08415{col 44}{space 1}    3.33{col 53}{space 3}0.001{col 61}{space 4} .1149941{col 74}{space 3} .4448561
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0115489{col 33}{space 2} .0074408{col 44}{space 1}    1.55{col 53}{space 3}0.121{col 61}{space 4}-.0030348{col 74}{space 3} .0261325
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.4763004{col 33}{space 2} .2624654{col 44}{space 1}   -1.81{col 53}{space 3}0.070{col 61}{space 4}-.9907232{col 74}{space 3} .0381224
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.493662{col 33}{space 2} .3799445{col 44}{space 1}   -3.93{col 53}{space 3}0.000{col 61}{space 4}-2.238339{col 74}{space 3}-.7489841
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}irr_exit            {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} .7163615{col 33}{space 2} .3069988{col 44}{space 1}    2.33{col 53}{space 3}0.020{col 61}{space 4}  .114655{col 74}{space 3} 1.318068
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2}  1.49269{col 33}{space 2} .2778675{col 44}{space 1}    5.37{col 53}{space 3}0.000{col 61}{space 4} .9480799{col 74}{space 3} 2.037301
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.1173186{col 33}{space 2} .2455075{col 44}{space 1}   -0.48{col 53}{space 3}0.633{col 61}{space 4}-.5985044{col 74}{space 3} .3638672
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.6314793{col 33}{space 2} .3038067{col 44}{space 1}   -2.08{col 53}{space 3}0.038{col 61}{space 4}-1.226929{col 74}{space 3}-.0360291
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .2314747{col 33}{space 2} .1030442{col 44}{space 1}    2.25{col 53}{space 3}0.025{col 61}{space 4} .0295117{col 74}{space 3} .4334377
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0097597{col 33}{space 2} .0143323{col 44}{space 1}   -0.68{col 53}{space 3}0.496{col 61}{space 4}-.0378506{col 74}{space 3} .0183312
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.5117601{col 33}{space 2} .2731232{col 44}{space 1}   -1.87{col 53}{space 3}0.061{col 61}{space 4}-1.047072{col 74}{space 3} .0235515
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.716782{col 33}{space 2} .4212775{col 44}{space 1}   -4.08{col 53}{space 3}0.000{col 61}{space 4}-2.542471{col 74}{space 3}-.8910936
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} 1.372615{col 33}{space 2} .1813376{col 44}{space 1}    7.57{col 53}{space 3}0.000{col 61}{space 4} 1.017199{col 74}{space 3}  1.72803
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .8792867{col 33}{space 2} .0411373{col 61}{space 4} .7687234{col 74}{space 3} .9388227
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of rho=0: chi2({res}1{txt}) = {res}57.2957                     {txt}Prob > chi2 = {res}0.0000
{txt}
{com}. est store m12 
{txt}
{com}. 
. biprobit punish irr_exit  max_pers_magaloni max_military_scale gwf_democracy  irr_entry  max_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{txt}Fitting comparison equation 1:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-224.53003}  
Iteration 1:{space 3}log pseudolikelihood = {res:-152.72366}  
Iteration 2:{space 3}log pseudolikelihood = {res:-150.82955}  
Iteration 3:{space 3}log pseudolikelihood = {res:-150.81954}  
Iteration 4:{space 3}log pseudolikelihood = {res:-150.81954}  
{res}
{txt}Fitting comparison equation 2:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-202.91014}  
Iteration 1:{space 3}log pseudolikelihood = {res:-130.28746}  
Iteration 2:{space 3}log pseudolikelihood = {res:-128.40805}  
Iteration 3:{space 3}log pseudolikelihood = {res:-128.39869}  
Iteration 4:{space 3}log pseudolikelihood = {res:-128.39869}  
{res}
{txt}Comparison:    log pseudolikelihood = {res}-279.21823

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-279.21823}  
Iteration 1:{space 3}log pseudolikelihood = {res:-231.59458}  
Iteration 2:{space 3}log pseudolikelihood = {res:-228.41821}  
Iteration 3:{space 3}log pseudolikelihood = {res:-228.33764}  
Iteration 4:{space 3}log pseudolikelihood = {res:-228.33757}  
Iteration 5:{space 3}log pseudolikelihood = {res:-228.33757}  
{res}
{txt}Bivariate probit regression{col 49}Number of obs{col 67}= {res}       460
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    218.83
{txt}Log pseudolikelihood = {res}-228.33757{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} .7554645{col 33}{space 2} .1753556{col 44}{space 1}    4.31{col 53}{space 3}0.000{col 61}{space 4} .4117738{col 74}{space 3} 1.099155
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .1239015{col 33}{space 2} .3400122{col 44}{space 1}    0.36{col 53}{space 3}0.716{col 61}{space 4}-.5425101{col 74}{space 3} .7903131
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} -.127777{col 33}{space 2} .2348429{col 44}{space 1}   -0.54{col 53}{space 3}0.586{col 61}{space 4}-.5880606{col 74}{space 3} .3325066
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.3247571{col 33}{space 2} .2830281{col 44}{space 1}   -1.15{col 53}{space 3}0.251{col 61}{space 4} -.879482{col 74}{space 3} .2299679
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .2621315{col 33}{space 2} .0774067{col 44}{space 1}    3.39{col 53}{space 3}0.001{col 61}{space 4} .1104171{col 74}{space 3} .4138459
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0054703{col 33}{space 2} .0080184{col 44}{space 1}    0.68{col 53}{space 3}0.495{col 61}{space 4}-.0102455{col 74}{space 3} .0211861
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.5694452{col 33}{space 2} .2843016{col 44}{space 1}   -2.00{col 53}{space 3}0.045{col 61}{space 4}-1.126666{col 74}{space 3}-.0122244
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.725513{col 33}{space 2} .3824129{col 44}{space 1}   -4.51{col 53}{space 3}0.000{col 61}{space 4}-2.475028{col 74}{space 3}-.9759972
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}irr_exit            {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2}  .659285{col 33}{space 2} .1627484{col 44}{space 1}    4.05{col 53}{space 3}0.000{col 61}{space 4} .3403039{col 74}{space 3}  .978266
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.118577{col 33}{space 2}  .342295{col 44}{space 1}    3.27{col 53}{space 3}0.001{col 61}{space 4} .4476913{col 74}{space 3} 1.789463
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .3795246{col 33}{space 2} .2654858{col 44}{space 1}    1.43{col 53}{space 3}0.153{col 61}{space 4} -.140818{col 74}{space 3} .8998672
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.4686857{col 33}{space 2} .2772097{col 44}{space 1}   -1.69{col 53}{space 3}0.091{col 61}{space 4}-1.012007{col 74}{space 3} .0746353
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .1911496{col 33}{space 2} .0947636{col 44}{space 1}    2.02{col 53}{space 3}0.044{col 61}{space 4} .0054165{col 74}{space 3} .3768828
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0158006{col 33}{space 2} .0152684{col 44}{space 1}   -1.03{col 53}{space 3}0.301{col 61}{space 4}-.0457261{col 74}{space 3} .0141249
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.5895266{col 33}{space 2} .2771857{col 44}{space 1}   -2.13{col 53}{space 3}0.033{col 61}{space 4}-1.132801{col 74}{space 3}-.0462525
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} -2.02902{col 33}{space 2}  .428916{col 44}{space 1}   -4.73{col 53}{space 3}0.000{col 61}{space 4} -2.86968{col 74}{space 3} -1.18836
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} 1.513728{col 33}{space 2} .2121662{col 44}{space 1}    7.13{col 53}{space 3}0.000{col 61}{space 4}  1.09789{col 74}{space 3} 1.929566
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .9075984{col 33}{space 2} .0373975{col 61}{space 4} .7997398{col 74}{space 3} .9586983
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of rho=0: chi2({res}1{txt}) = {res}50.903                      {txt}Prob > chi2 = {res}0.0000
{txt}
{com}. est store m13
{txt}
{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}F:\Mussolini\FPA RnR v2\Main Tables and Figures\Main Tables Log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}17 Sep 2018, 12:13:51
{txt}{.-}
{smcl}
{txt}{sf}{ul off}